Bottom Line:
A crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards.Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification.Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification.

Affiliation: Department of Computer Science, Texas A&M University, College Station, Texas, United States of America.

ABSTRACTA crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards. The current pratice, visual examination by human analysts, is time consuming and requires several years of experience. Here we developed a species identification algorithm which utilizes images of microscopic elytra fragments. The elytra, or hardened forewings, occupy a large portion of the body, and contain distinctive patterns. In addition, elytra fragments are more commonly recovered from processed food products than other body parts due to their hardness. As a preliminary effort, we chose 15 storage product beetle species frequently detected in food inspection. The elytra were then separated from the specimens and imaged under a microscope. Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification. With leave-one-out cross validation, we achieved overall accuracy of 80% through the proposed global and local features, which indicates that our proposed features could differentiate these species. Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification. Future work will include robust testing with more beetle species and algorithm refinement for a higher accuracy.

pone.0157940.g001: The workflow of food contaminating beetle species identification by image analysis.

Mentions:
Our algorithm for beetle species identification is outlined in Fig 1. As is typical for digital image processing and pattern recognition/classification, the envisioned workflow includes step-by-step procedures: preprocessing, feature extraction, and classification. As shown in Fig 1, the captured elytra fragment image is first Gaussian-filtered and histogram-equalized for quality enhancement. Then, global and local appearance characteristics are quantified as features, which are aggregated as a final feature vector. Finally, through normalization of all independent features, the (input) feature vector is fed into a neural network classifier for training. The rest of this section describes in detail data acquisition, observed elytra patterns (types), feature extraction, and classification.

pone.0157940.g001: The workflow of food contaminating beetle species identification by image analysis.

Mentions:
Our algorithm for beetle species identification is outlined in Fig 1. As is typical for digital image processing and pattern recognition/classification, the envisioned workflow includes step-by-step procedures: preprocessing, feature extraction, and classification. As shown in Fig 1, the captured elytra fragment image is first Gaussian-filtered and histogram-equalized for quality enhancement. Then, global and local appearance characteristics are quantified as features, which are aggregated as a final feature vector. Finally, through normalization of all independent features, the (input) feature vector is fed into a neural network classifier for training. The rest of this section describes in detail data acquisition, observed elytra patterns (types), feature extraction, and classification.

Bottom Line:
A crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards.Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification.Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification.

Affiliation:
Department of Computer Science, Texas A&M University, College Station, Texas, United States of America.

ABSTRACTA crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards. The current pratice, visual examination by human analysts, is time consuming and requires several years of experience. Here we developed a species identification algorithm which utilizes images of microscopic elytra fragments. The elytra, or hardened forewings, occupy a large portion of the body, and contain distinctive patterns. In addition, elytra fragments are more commonly recovered from processed food products than other body parts due to their hardness. As a preliminary effort, we chose 15 storage product beetle species frequently detected in food inspection. The elytra were then separated from the specimens and imaged under a microscope. Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification. With leave-one-out cross validation, we achieved overall accuracy of 80% through the proposed global and local features, which indicates that our proposed features could differentiate these species. Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification. Future work will include robust testing with more beetle species and algorithm refinement for a higher accuracy.